Dai Hao, Huang Yu, Liu Yuxi, He Xing, Guo Jingchuan, Prosperi Mattia, Bian Jiang
Department of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA.
J Biomed Inform. 2025 Jul 21;169:104880. doi: 10.1016/j.jbi.2025.104880.
By leveraging real-world electronic health record (EHR) data, this study set out to estimate individualized treatment effects (ITE) in longitudinal observational settings to advance personalized medicine, addressing key challenges that are often observed in real-world clinical scenarios and pose statistical challenges, including hidden confounding and dynamic treatment regimens.
We propose the Variational Temporal Deconfounder Network (VTDNet), a novel framework designed to account for time-varying hidden confounding using a variational recurrent transformer-based autoencoder. Specifically, VTDNet comprises three critical components: a temporal Encoder-Decoder structure to capture hidden representation, a Treatment Block that captures interdependencies among multiple treatments, and a Potential Outcome Block that predicts both factual and counterfactual outcomes. We assess the effectiveness of the proposed framework using a synthetic dataset and two real-world datasets: MIMIC-III, an EHR dataset focusing on intensive care settings, and NACC, emphasizing neurodegenerative disease, collected using a standardized protocol from participants enrolled in Alzheimer's Disease Research Center (ADRC) clinical cores.
Experimental results on the synthetic dataset demonstrate superior accuracy under varying levels of confounding. On real-world EHR datasets, VTDNet achieves lower root mean squared error, mean absolute error, and influence function precision in the estimation of heterogeneous effects compared to existing state-of-the-art methods.
The proposed VTDNet offers a robust framework for estimating individualized treatment effects in longitudinal settings, effectively accommodating irregular time points and high-dimensional data while addressing hidden confounders through a deep generative approach. It holds significant potential to advance personalized medicine and support real-world evidence generation. Future work will aim to extend VTDNet to continuous treatment scenarios, such as dose-response analysis, to further broaden its applicability in clinical practice.
通过利用真实世界的电子健康记录（EHR）数据，本研究旨在估计纵向观察环境中的个体化治疗效果（ITE），以推进个性化医疗，解决在真实世界临床场景中经常观察到的并带来统计挑战的关键问题，包括隐藏混杂因素和动态治疗方案。
我们提出了变分时间去混杂网络（VTDNet），这是一个新颖的框架，旨在使用基于变分循环变压器的自动编码器来处理随时间变化的隐藏混杂因素。具体而言，VTDNet包含三个关键组件：一个用于捕获隐藏表示的时间编码器 - 解码器结构、一个捕获多种治疗之间相互依赖性的治疗模块，以及一个预测事实和反事实结果的潜在结果模块。我们使用一个合成数据集和两个真实世界数据集评估所提出框架的有效性：MIMIC - III，一个专注于重症监护环境的EHR数据集，以及NACC，该数据集强调神经退行性疾病，是通过标准化协议从参与阿尔茨海默病研究中心（ADRC）临床核心的参与者中收集的。
合成数据集上的实验结果表明，在不同程度的混杂情况下，该方法具有更高的准确性。在真实世界的EHR数据集上，与现有的最先进方法相比，VTDNet在估计异质效应时实现了更低的均方根误差、平均绝对误差和影响函数精度。
所提出的VTDNet为估计纵向环境中的个体化治疗效果提供了一个强大的框架，有效地适应不规则时间点和高维数据，同时通过深度生成方法解决隐藏混杂因素。它在推进个性化医疗和支持真实世界证据生成方面具有巨大潜力。未来的工作将旨在将VTDNet扩展到连续治疗场景，如剂量 - 反应分析，以进一步扩大其在临床实践中的适用性。
